The first research about predicting monocular depth is Saxena's work [1]}, which introduced Markov Random Field(MRF) to exploit the geometic priors. Later on, with the appearance of convolutional neural network(CNN), Eigen et al. [2]} proposed a model intergrating the global and local information of a single image. Since then, a large number of studies using deep learning methods have emerged. Liu et al. [3]} combined Convolutional Neural Fields(CRFs) with fully convolutional networks and put foward a superpixel pooling method making the inference faster. Laina et al. [4]} proposed a fully convolutional residual architecture and four up-sampling models to restore the resolution of depth map. To reduce the information loss induced by excessive pooling, Fu et al. [5]} employed the atrous spatial pyramid pooling (ASPP) and presented an ordinal loss to model the depth prediction as an ordinal regression problem. Inspired by [5]}, Chen et al. [7]} proposed soft ordinal inference to exploit the predicted probabilities of the whole depth intervals and replaced ASPP with self-attention module to capture the global context. Recently, Yin et al. [8]} projected the depth map to obtain the 3D point cloud, exploiting the loss between virtual normal and ground truth to train the model, which significantly improved the accuracy. There were also some works [9]}[10]}[11]} who employed the geometric constraints of the consective image sequence to complete unsupervised depth prediction.
